(I) The convolutional layer in CNN architecture consists of multiple convolutional filters. These filters are also known as kernels. Convolution operation is performed between the raw data that is in the form of a matrix and these kernels that generate an output feature map. The numbers present in the kernel is the kernel weight of the kernel. The initial values of the kernel are random in nature, during the training process the kernel values are adjusted to help in extracting important features from the data. In convolutional operation the CNN input format description is present. In convolution operation let’s say in 10\(*\) 10 grey-scale image a randomly initialized kernel slides vertically and horizontally and the dot product between them is computed. In 1D-CNN the kernel function moves in one direction only. Similarly, in 2D-CNN and 3D-CNN the kernel function moves in two and three directions respectively. The computed values are multiplied to create a single scalar value.
The data processed by the kernel of CNN sometimes may require padding. It is a process of extracting border information from the input data. Padding refers to the adding layers of extra pixels (zeros) to the input data that helps to preserve information present on the borders[1]}.
